Open Access

ARTICLE

Frilled Lizard Optimization: A Novel Bio-Inspired Optimizer for Solving Engineering Applications

by Ibraheem Abu Falahah¹, Osama Al-Baik², Saleh Alomari³, Gulnara Bektemyssova⁴, Saikat Gochhait^5,6, Irina Leonova⁷, Om Parkash Malik⁸, Frank Werner^9,*, Mohammad Dehghani¹⁰

1 Department of Mathematics, Faculty of Science, The Hashemite University, P.O. Box 330127, Zarqa, 13133, Jordan
2 Department of Software Engineering, Al-Ahliyya Amman University, Amman, 19328, Jordan
3 Faculty of Science and Information Technology, Software Engineering, Jadara University, Irbid, 21110, Jordan
4 Department of Computer Engineering, International Information Technology University, Almaty, 050000, Kazakhstan
5 Symbiosis Institute of Digital and Telecom Management, Constituent of Symbiosis International Deemed University, Pune, 412115, India
6 Neuroscience Research Institute, Samara State Medical University, Samara, 443001, Russia
7 Faculty of Social Sciences, Lobachevsky University, Nizhny Novgorod, 603950, Russia
8 Department of Electrical and Software Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
9 Faculty of Mathematics, Otto-von-Guericke University, P.O. Box 4120, Magdeburg, 39016, Germany
10 Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, 7155713876, Iran

* Corresponding Author: Frank Werner. Email:

(This article belongs to the Special Issue: Metaheuristic-Driven Optimization Algorithms: Methods and Applications)

Computers, Materials & Continua 2024, 79(3), 3631-3678. https://doi.org/10.32604/cmc.2024.053189

Received 20 April 2024; Accepted 23 May 2024; Issue published 20 June 2024

Abstract

This research presents a novel nature-inspired metaheuristic algorithm called Frilled Lizard Optimization (FLO), which emulates the unique hunting behavior of frilled lizards in their natural habitat. FLO draws its inspiration from the sit-and-wait hunting strategy of these lizards. The algorithm’s core principles are meticulously detailed and mathematically structured into two distinct phases: (i) an exploration phase, which mimics the lizard’s sudden attack on its prey, and (ii) an exploitation phase, which simulates the lizard’s retreat to the treetops after feeding. To assess FLO’s efficacy in addressing optimization problems, its performance is rigorously tested on fifty-two standard benchmark functions. These functions include unimodal, high-dimensional multimodal, and fixed-dimensional multimodal functions, as well as the challenging CEC 2017 test suite. FLO’s performance is benchmarked against twelve established metaheuristic algorithms, providing a comprehensive comparative analysis. The simulation results demonstrate that FLO excels in both exploration and exploitation, effectively balancing these two critical aspects throughout the search process. This balanced approach enables FLO to outperform several competing algorithms in numerous test cases. Additionally, FLO is applied to twenty-two constrained optimization problems from the CEC 2011 test suite and four complex engineering design problems, further validating its robustness and versatility in solving real-world optimization challenges. Overall, the study highlights FLO’s superior performance and its potential as a powerful tool for tackling a wide range of optimization problems.

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